Deep Learning Basics

While reading Hung Lee’s Recruiting Brainfood, I stumbled upon this deep learning primer:

The Simple Guide to Deep Learning

The primer is great, and a quick read. Here is my quick summary below:

The basics of deep learning is to think about how the brain breaks up a specific task. For example, let’s say you are hiking the Appalachian Trail, and you see something in the distance running towards you. First, you might notice it is moving. Then, you might notice what shape it is. Then, you might notice how fast it is going. Then, you might notice a big snout. Then, your brain will determine that this is an animal.

The process would continue until your brain evaluated, classified and predicts what object it is seeing. The joy of the mental exercise (for me) is to understand how the human mind works to break down ideas.

Inputs > Algorithm > Prediction > Training: 

The following are the key concepts for thinking about deep learning concepts. Yes, this is overly simplified, but it is still a helpful start. 

  • Inputs: Labels/Images
  • Algorithm: 
    • Levels of Abstraction 1: Is this a shape?
    • Level of Abstraction 2: Is this shape an ear?
    • Level of Abstraction 3: Is this a cat?
  • Prediction = Yes or No. Is this prediction correct?

Current-State of Deep Learning:

  • Supervised Deep Learning: In effect, this is attempting to clone human behavior via labeled images, video, text or speech. 
  • Reinforcement Learning: This is where the model attempts to “learn” behaviors, codify those behaviors (i.e. what does that mean), and then implement strategies to optimize based on those strategies. As the article suggests, the following are some examples:
    • E-Commerce: model learns customer behaviors and tailors service to suit customer interests. 
    • Finance: model learns market behavior and generates trading strategies. 
    • Robots: model learns how physical world behaves (through video) and then navigates that world.

Network Architecture to Detect Objects in Images:

  • Input: Image
  • Extract Feature: Extract the specific features
  • Classification: Classify based on the probability of those features
  • Output: Image prediction

Enjoy your deep learning explorations!

AI & Machine Learning and Knowledge Work

AI and Machine Learning will slowly arrive make its way into all of our apps in both seen and unseen ways.

How do we embrace this? How do we plan for this? How will this change the work all of us do?

Here’s Microsoft’s “Design Ideas” in PowerPoint:

What is the role of AI in radiology?

In the article, “New AI Can Diagnose Pneumonia Better Than Doctors (https://www.fastcodesign.com/90152230/new-ai-can-diagnose-pneumonia-better-than-doctors) we begin to see a glimpse of the possibilities:

“In the case of CheXnet, the research team led by Stanford adjunct professor Andrew Ng, started by training the neural network with 112,120 chest X-ray images that were previously manually labeled with up to 14 different diseases. One of them was pneumonia. After training it for a month, the software beat previous computer-based methods to detect this type of infection. The Stanford Machine Learning Group team pitted its software against four Stanford radiologists, giving each of them 420 X-ray images. This graphic shows how the radiologists–represented by the orange Xs–did compared to the program–represented by the blue curve.”

[Article: FastCoDesign.com]

[Image: Stanford Machine Learning Group]

Machine Learning vs. IoT Sensors for Finding Parking Spots

Some of my favorite work with cities and companies is around the Internet of Things. There are many modern day challenges that are best solved with technology. However, the question that we face is how best to solve those challenges. 

For example, some cities, are exploring adding sensors to parking spaces to help inform motorists about where parking spots are available. This type of endeavor will be expensive in so many different ways. Any city looking to do this, will likely need to architect some type of system that looks like the following: 

On the other hand, Google, is leveraging its maps data too help predict where you are most likely to find parking. This is an impressive use of machine learning instead of on-the-ground sensors for smart parking.

Using Machine Learning to Predict Parking Difficulty